How global business leaders can harness the power of GenAI

We find that while 43% of CEOs have already begun investing in AI and another 45% plan to do so in the next year, according to the EY CEO Outlook Pulse survey, many are pursuing quick efficiency gains rather than more fundamental changes to enhance GenAI’s growth potential.

This gradualist approach is not surprising as it aligns with prior technological revolutions. Indeed, there are often numerous sources of delay between a technological revolution and its boost to economic growth:

• Learning and adjustment period. With any new technology, there is often a period during which it’s essential for business leaders to develop an understanding of its potential benefits, and workers and managers to learn how to use it effectively. This often involves trial and error by early adopters, training of personnel and the development of leading practices.
  
  
• Implementation and diffusion. Subsequently, it takes time for new technologies to be adopted and diffused throughout an economy. Even after a technology is introduced, businesses might delay the adoption due to high up-front costs, uncertainty about its benefits or simply because they are waiting to see if even better technologies emerge.
  
  
• Complementary innovations. Some technologies require complementary innovations or infrastructures to be fully effective. For instance, while the steam engine was invented in the early 1700s, it wasn’t until about 80 years later that steam technology improved and was adopted more broadly. Similarly, while the electrical age began in the 1880s in the US, it was only in the 1920s when electrification made critical advances, especially with more robust electrical grids. And the benefits of personal computers were magnified once the internet became widespread.

The notable capabilities and performance of emerging AI tools already represent a big improvement over those introduced a decade ago, such as cell phone virtual assistants. And while broad-based productivity boost will likely occur with a lag, the speed of technological adoption and diffusion has increased from multiple decades in the 1800s to around 10 years in the computer age. Faster diffusion and adoption of GenAI could mean the boost to economic activity will be felt in the next three to five years.

The US economy is currently leading the way in terms of AI research and development, commercialization and talent, but prospects for growth in other economies are very encouraging. Classifying different economies as a function of three dimensions — the number of AI companies, the number of AI patents granted and the number of AI professionals — illustrates this reality.

Based on EY-Parthenon analysis, the US is benefiting from both a first-mover advantage as well as demographic size with the number of AI companies (17.7k) and patents (41.4k) being about 10 times larger than in most other economies and the largest number of AI professionals (22.5k). China is in an interesting position. While it has much fewer AI companies (1.9k), it benefits from having the second-largest global research and development pipeline with about 75% of the US number of AI patents (29.4k).

India is also noteworthy as it stands in line with other advanced economies with regards to the number of AI patents (2.9k) and companies (2.7k), but it benefits from a clear scale advantage in the second-largest number of AI professionals (13.6k), behind the US (22.5k). A key question is whether India will retain and leverage that skilled talent pool domestically.

When combining the contribution from greater investment in GenAI and stronger productivity growth, one can truly observe the potential of GenAI to significantly lift global economic activity over the next decade.

We estimate the GenAI revolution could lift global GDP by US$1.7t to US$3.4t in 2033, or the equivalent of 1.5% to 3%. For the US economy, the boost to GDP will likely range from US$900b to US$1.7t by 2033, or the equivalent of 3.5% to 7%.

These figures might not mean much to the average observer, but assuming the US economy’s long-term growth rate is around 1.8% to 2.0%, this would represent the equivalent of two to four extra years of growth within a decade.

At the global level, where long-term GDP growth is closer to 2.5% to 3.0%, this would represent up to a full year of additional growth over the next decade — or, put differently, adding an economy the size of India over a decade.

Capital investment foundation

In assessing the potential economic impact of GenAI from a capital investment perspective, we examined the near-term boost to growth from increased investment in software, R&D in semiconductor and other electronic components manufacturing, other computer and electronic product manufacturing, scientific services and software publishers, computers and peripheral equipment, and communication equipment.

Drawing parallels with the IT revolution of 1980-2000, we found that business investment in GenAI will likely be 25% faster than the 2017-2022 annual trend pace of growth of 8.8%. This would lead to a real GDP lift of 1%, worth over US$250b over the next decade. A more optimistic scenario could see 50% faster business investment growth, leading to a real GDP boost of 2%, worth a cumulative US$500b by 2033.

Looking across major economies, the contribution from greater GenAI investment could also be significant. While the US market is likely to remain the leader in GenAI technologies investment, China, Europe and India will most likely be following closely behind. We estimate that the lift to global GDP could total between US$500b and US$1t over the next decade.

Productivity growth acceleration

During the information and communication technologies (ICT) revolution of 1980s-1990s, productivity growth doubled. Using a conservative assumption that the productivity growth lift from GenAI is only half of productivity acceleration witnessed during the ICT revolution of 1980s-1990s, we find that it would raise GDP by US$650b over the next decade, or the equivalent of a 2.5% increase by 2033.

In a more optimistic scenario where productivity grows twice as fast as the 2017-2022 trend pace – akin to the acceleration during the computer age – the productivity boost powered by GenAI would lift GDP by US$1.2t, or 5%, over the next 10 years.

Looking across major economies, a GenAI-driven productivity upswing could also make a substantial contribution to the global economy. We estimate that the lift to global GDP from stronger productivity could total US$1.2t to US$2.4t over the next decade.

We believe the GenAI-driven labor transformation will be universal in that it will affect nearly all functions over the coming decade, but the degree to which functions will be affected will vary greatly across occupations, sectors and geographies. In the US, 66% of employment (or the equivalent of 104m jobs) is highly or moderately exposed to GenAI. The remaining 34% of occupations have low AI exposure but will likely still be affected by AI via some secondary tasks. Globally, we find that 59% of occupations have a “high to moderate” exposure, with 67% in advanced economies and 57% in emerging markets.

GenAI’s ability to perform complex cognitive tasks means that AI taking over and rendering low-skilled jobs obsolete will most likely turn out to be a myth, and advanced economies may initially see a greater labor augmentation potential. Mathematical and programming skills have the highest GenAI exposure scores, while active listening and learning have the lowest, as these are tasks involving complex human interaction, creativity and emotional understanding.

Looking at the top 10 occupation groupings with the highest AI augmentation scores, professions like plant and system operators, physical scientists, agricultural workers, drafters, programmers, engineers and architects involve a high degree of repetitive and data-driven tasks that AI can automate. These include data analysis and monitoring, operations scheduling, document review, design work and safety inspection processes.

Although these occupations are significantly exposed to AI, total automation is unlikely, as workers remain indispensable for overseeing processes, strategic decision-making and tasks requiring nuanced judgment. Indeed, the wide dispersion of AI augmentation scores for the top 10 occupation groupings illustrates the importance of human intervention.

The 10 occupation groupings with the lowest AI augmentation scores have the highest intrinsic human elements needed for their functions. For example, roles like post-secondary teachers require human interaction and ability to customize learning. Occupations such as health care practitioners demand personalized care and critical decisions that AI currently can’t provide. Roles such as cooks and firefighters necessitate precise physical intervention (which can require complementary robotization) and high-stakes decision-making in unpredictable situations. Consequently, these roles, which require human interaction, decision-making, physical intervention and personalization, face lower risk from GenAI augmentation at first.

Still, as is the case for major occupations with high AI exposure scores, there is wide dispersion across the low-AI-exposure occupations. Even occupations that require human interaction have some sub-functions that can be augmented via GenAI. Think of grading or course material preparation for a teacher; or prevention, monitoring and early risk detection for firefighters.

The integration of GenAI also brings challenges and risks. These could manifest in several ways, such as rising income inequality, increasing market concentration and widening global disparities. Addressing these issues effectively is crucial to leveraging GenAI capabilities in a manner that inclusively benefits households, companies and economies across the globe.

"Economically, GenAI’s impact is immense. With governing frameworks for adoption, business leaders who take a human-centric, responsible approach can address inequality and expand prosperity for shareholders and the broader economy,” Khan says.

The uplift from AI will not likely be equally felt among households. In the case of the US, we estimate that the economic gains from GenAI will translate into a boost to household income worth between US$675b and US$1.3t over the next decade. Over 50% of the gain will accrue to households in the upper income quintile (top 20% of households by income level) while less than 5% of it will accrue to the bottom quintile (bottom 20%). Globally, the effects on inequality could be similarly biased, with higher-income workers benefiting from outsized gains, especially in economies where inequality and income polarization are high. 

Wage inequality will likely increase as workers in high-paid occupations stand to capture a greater share of labor income gains. With GenAI showing the greatest potential to complement (rather than substitute) high-wage occupations relative to low-wage occupations, higher-wage workers are likely to see a disproportionate increase in their labor income, which could lead to a widening in inequality.

GenAI development also has the potential to spur greater market concentration and create winner-takes-all business dynamics. GenAI technologies offer first-mover advantages and large economies of scale that could lead to a growing divide between AI leaders and laggards and the rise of “superstar” firms that could reap most of the AI benefits. The risk of oligopolies and vertical integration raises the stakes for regulators to offset potential negative externalities.

The global economic boost from AI will likely be concentrated in countries at the forefront of AI development and those best equipped to harness the technologies. Pioneers in AI development such as the US and China and early adopters such as the UK, Canada, Japan, South Korea and India will most likely benefit disproportionately from AI’s economic boost while developing countries that are the least prepared for AI adoption such as those in sub-Saharan Africa, Latin America and South Asia will likely lag behind.

Addressing these AI divides will require a multifaceted approach. At the workforce level, it will be critical to implement policies that help mitigate the less desirable impacts of AI on workers such as job displacement as well as supporting training and upskilling of the workforce to enable these new technologies.

At the country level, encouraging the diffusion of new technologies among smaller firms and implementing business strategies and policies that promote increased market competition could help to spread AI’s benefits more broadly across the economy.

At the global level, stronger cooperation to reduce technological disparities by expanding access to AI technology and infrastructure and building digital skills could help bridge the AI divide.

Concluding thoughts

In today's rapidly evolving and complex global landscape — characterized by slow productivity growth, aging populations, delicate supply chains and growing geopolitical tensions — the power of GenAI stands out as a beacon for business leaders. It presents an innovative way to navigate these challenges by enhancing operational efficiency and sparking unparalleled innovation.

However, the adoption of GenAI is not without its hurdles and potential pitfalls. Prior technological revolutions have illustrated the importance of building the ecosystem of complementary innovations, infrastructure, skills and culture necessary to fully reap the productivity benefits of this new technology.

GenAI’s integration into the fabric of business and society could also lead to increased income inequality, greater market dominance by a few and expanded global disparities. Tackling these challenges head-on is imperative for the advantages of GenAI to be distributed equitably, benefiting individuals, businesses and economies worldwide in an inclusive manner.